Co-flowering and hummingbird overlap in Neotropical Gesneriaceae: Predicting Phenology
Ben Weinstein
6/4/2018
1 Transects
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
2 Phylogeny
2.0.1 Total Flowers
Peak date
3 Count model of species phenology
3.1 Infer absences
3.2 Julian Day
## sink("model/julian.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #observation
## log(phi[x])<-alpha[Plant[x]] + beta[Plant[x]] * julian[x] + beta2[Plant[x]] * julian[x]^2
##
## Yobs[x] ~ dpois(phi[x])
##
## }
##
## #phylogenetic covariance among plants
## #covert variance to precision
## #tauC[1:Plants,1:Plants]=inverse((sigma^2)*C[,])
##
## #e[1:Plants] ~ dmnorm(zeros[],tauC[,])
##
## #Effect of phylogeny, calculate pagels lambda using variance-covariance matrix and identity matrix
## #C[1:Plants,1:Plants] = lambda * vcov[,] + (1-lambda) * I[,]
##
## #Assess Model Fit
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #poisson regression prior
## beta[j] ~ dnorm(0,0.0001)
## beta2[j] ~ dnorm(0,0.0001)
## alpha[j] ~ dnorm(0,0.0001)
## }
##
## #pagels lambda
## #lambda ~ dbeta(1,1)
##
## #variance in phylogenetic effect
## #sigma ~ dunif(0,10)
##
## #Prediction
##
##
## }
## ",fill=TRUE)
##
## sink()## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 4267
## Unobserved stochastic nodes: 51
## Total graph size: 25691
##
## Initializing model4 Get Chains
4.0.1 Evaluate convergence
4.0.2 Posterior estimates
4.0.3 Effect of Julian day
4.1 Julian + Phylogenetic Attraction
## sink("model/julian_phylogeny.jags")
## cat("
## model {
##
## #observation - species specific intercept and phylogenetic covariance in julian day
##
## for (x in 1:Nobs){
##
## log(phi[x])<- alpha[Plant[x]] + beta[Plant[x]] * julian[x] + beta2[Plant[x]] * julian[x]^2
##
## Yobs[x] ~ dpois(phi[x])
##
## }
##
## #species-specific responses to julian day. Polynomial model.
##
## #beta 1
## beta[1:Plants] ~ dmnorm(zeros[],tauC[,])
## beta2[1:Plants] ~ dmnorm(zeros[],tauC2[,])
##
## ##Phylogenetic covariance in effect of julian day
## #Effect of phylogeny, calculate pagels lambda using variance-covariance matrix and identity matrix
##
## C[1:Plants,1:Plants] = lambda * vcov[,] + (1-lambda) * I[,]
##
## ## Since the response to julian is X^2 polynomial, need phylogenetic effects on both terms.
## ## These terms share everything except for the scaling factor sigma. The phylogenetic signal is the same.
## ## covert variance to precision for each parameter
##
## tauC[1:Plants,1:Plants]=inverse((sigma^2)*C[,])
## tauC2[1:Plants,1:Plants]=inverse((sigma2^2)*C[,])
##
## ## **************************
## ## Priors
## ## **************************
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## alpha[j] ~ dnorm(0,0.0001)
## }
##
## #pagels lambda
## lambda ~ dbeta(1,1)
##
## #variance in phylogenetic effect
## #beta term
## sigma ~ dunif(0,10)
## #beta2 term
## sigma2 ~ dunif(0,10)
##
##
##
## }
## ",fill=TRUE)
##
## sink()## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3514
## Unobserved stochastic nodes: 19
## Total graph size: 21617
##
## Initializing model5 Get Chains
5.0.1 Evaluate convergence
5.0.2 Posterior estimates
5.0.3 Effect of Julian day
5.1 Julian + Phylogenetic Repulsion
## sink("model/julian_phylogeny.jags")
## cat("
## model {
##
## #observation - species specific intercept and phylogenetic covariance in julian day
##
## for (x in 1:Nobs){
##
## log(phi[x])<- alpha[Plant[x]] + beta[Plant[x]] * julian[x] + beta2[Plant[x]] * julian[x]^2
##
## Yobs[x] ~ dpois(phi[x])
##
## }
##
## #species-specific responses to julian day. Polynomial model.
##
## #beta 1
## beta[1:Plants] ~ dmnorm(zeros[],tauC[,])
## beta2[1:Plants] ~ dmnorm(zeros[],tauC2[,])
##
## ##Phylogenetic covariance in effect of julian day
## #Effect of phylogeny, calculate pagels lambda using variance-covariance matrix and identity matrix
##
## C[1:Plants,1:Plants] = lambda * vcov[,] + (1-lambda) * I[,]
##
## ## Since the response to julian is X^2 polynomial, need phylogenetic effects on both terms.
## ## These terms share everything except for the scaling factor sigma. The phylogenetic signal is the same.
## ## covert variance to precision for each parameter
##
## tauC[1:Plants,1:Plants]=inverse((sigma^2)*C[,])
## tauC2[1:Plants,1:Plants]=inverse((sigma2^2)*C[,])
##
## ## **************************
## ## Priors
## ## **************************
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## alpha[j] ~ dnorm(0,0.0001)
## }
##
## #pagels lambda
## lambda ~ dbeta(1,1)
##
## #variance in phylogenetic effect
## #beta term
## sigma ~ dunif(0,10)
## #beta2 term
## sigma2 ~ dunif(0,10)
##
##
##
## }
## ",fill=TRUE)
##
## sink()## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 3514
## Unobserved stochastic nodes: 19
## Total graph size: 21617
##
## Initializing model6 Get Chains
6.0.1 Evaluate convergence
6.0.2 Posterior estimates
6.0.3 Effect of Julian day
7 Model Comparison
7.1 Effect of julian day
8 Model Fit
8.1 Overall
8.2 By Species
9 Prediction
Todo